Building automated vandalism detection tools for Wikidata

Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89% of vandalism while reducing patrollers' workload by 98%, by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit

Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers

Current research on recommendation systems focuses on optimization and evaluation of the quality of ranked recommended results. One of the most common approaches used in digital paper libraries to present and recommend relevant search results, is ranking the papers based on their features. However, feature utility or relevance varies greatly from highly relevant to less relevant, and redundant. Departing from the existing recommendation systems, in which all item features are considered to be equally important, this study presents the initial development of an approach to feature weighting with the goal of obtaining a novel recommendation method in which features which are more effective have a higher contribution/weight to the ranking process. Furthermore, it focuses on obtaining ranking of results returned by a query through a collaborative weighting procedure carried out by human users. The collaborative feature-weighting procedure is shown to be incremental, which in turn leads to an incremental approach to feature-based similarity evaluation. The obtained system is then evaluated using Normalized Discounted Cumulative Gain (NDCG) with respect to a crowd-sourced ranked results. Comparison between the performance of the proposed and Ranking SVM methods shows that the overall ranking accuracy of the proposed approach outperforms the ranking accuracy of Ranking SVM method.ISSN:0975-900XISSN:0976-219

PROXIMITY-BASED ADAPTATION OF CONTENT TO GROUPS OF VIEWERS OF PUBLIC DISPLAYS

<p>Responsive design adapts web content to different viewing contexts to deliver an optimal viewing and interaction experience. Recent work proposed a model and framework for proximity-based adaptation of web content as a new dimension for responsive web design. While it was shown that the model improves the perception and user engagement for single viewers, until now, the effect had not been investigated for multiple simultaneous viewers who may be at different distances from the display. In this paper, we report on an initial study that evaluated and compared the effects of using the average distance of viewers as the basis for handling adaptation of content to multiple viewers with a classic one that adapts content based only on display characteristics. Our results show that the adaptive model provides a better view of the content and improves user engagement, but can be confusing when serving multiple viewers</p

Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers

Current research on recommendation systems focuses on optimization and evaluation of the quality of ranked recommended results. One of the most common approaches used in digital paper libraries to present and recommend relevant search results, is ranking the papers based on their features. However, feature utility or relevance varies greatly from highly relevant to less relevant, and redundant. Departing from the existing recommendation systems, in which all item features are considered to be equally important, this study presents the initial development of an approach to feature weighting with the goal of obtaining a novel recommendation method in which features which are more effective have a higher contribution/weight to the ranking process. Furthermore, it focuses on obtaining ranking of results returned by a query through a collaborative weighting procedure carried out by human users. The collaborative feature-weighting procedure is shown to be incremental, which in turn leads to an incremental approach to feature-based similarity evaluation. The obtained system is then evaluated using Normalized Discounted Cumulative Gain (NDCG) with respect to a crowd-sourced ranked results. Comparison between the performance of the proposed and Ranking SVM methods shows that the overall ranking accuracy of the proposed approach outperforms the ranking accuracy of Ranking SVM method

Monthly Wikipedia article quality predictions

Machine predicted quality levels of all articles in Wikipedia on a monthly basis.  All datasets contain the following 6 columns.<div><br><ul><li>page_id -- The page identifier<br></li><li>page_title -- The title of the article (UTF-8_with_underscores)<br></li><li>rev_id -- The most recent revision ID at the time of assessment<br></li><li>timestamp -- The timestamp when the assessment was taken (YYYYMMDDHHMMSS) <br></li><li>prediction -- The predicted quality class ("Stub", "Start", "C", "B", "GA", "FA", ...)<br></li><li>weighted_sum -- The sum of prediction weights assuming indexed class ordering ("Stub" = 0, "Start" = 1, ...)</li></ul><div>Predictions are made using the ORES "wp10" models for the relevant language.  See [1] and [2] for more information.</div></div

RDspeed: development framework for speed-based adaptation of web content on public displays

Viewers of public displays perceive the content of a display at different walking speeds. While responsive design (RD) adapts web content to different viewing contexts, so far only the characteristics of the device and recently the proximity of the viewers are taken into account. Yet, little attention has been paid to speed-based adaption of content and its potential in case of public displays. We therefore decided to develop a framework that would support speed-based adaptation of public display applications. In this paper, we present a framework called RDSpeed that allows developers and designers alike to easily utilize our speed-based adaptation technique and integrate them into their own applications. RDSpeed extends the standard RD definition by adding new media queries for each adaptation technique. Media queries have long been established as the go-to technique for developing responsive web applications when dealing with a variety of different devices. A user study was conducted to investigate the potential of our content adaptation technique, and possible use and extensions in the future. We show several example adaptive applications of RDSpeed, as well as discussing advantages and limitations of our framework as revealed by our user study

Chained Displays : Configuration of Multiple Co-Located Public Displays

ISSN:0974-9322ISSN:0975-229